Our research aims to uncover the mechanisms driving tumor metastasis, tumor progression and therapy resistance by developing and applying imaging-guided omics technologies. We integrate advanced imaging, computational analysis, and single-cell & spatial omics profiling to achieve this goal.
Tumor Metastasis
We investigate metastasis through two complementary approaches: the intrinsic behaviors of metastatic cancer cells, and the influence of the tumor microenvironment (TME). To study cell-intrinsic mechanisms, we developed (proprietary) platforms such as Functional single-cell sequencing (FUNseq) and Behavior-coupled sequencing (BC-seq), which enable behavior-coupled transcriptomics by identifying, isolating, and sequencing migratory cancer subpopulations. These approaches have led to the discovery of a novel predictive gene signature for metastasis in head and neck squamous cell carcinoma (HNSCC), as well as novel metastasis-associated mechanisms and druggable targets.
To assess TME contributions, we developed a spatially resolved single-cell sequencing method to analyze immune cell composition and pathway modulation within immune niches of metastatic vs. non-metastatic HNSCC tumors. To further enable spatial profiling of cells based on live-cell dynamics or multiple phenotypes, we are developing in situ FUNseq and multi-FUNseq platforms.
We investigate metastasis through two complementary approaches: the intrinsic behaviors of metastatic cancer cells, and the influence of the tumor microenvironment (TME). To study cell-intrinsic mechanisms, we developed (proprietary) platforms such as Functional single-cell sequencing (FUNseq) and Behavior-coupled sequencing (BC-seq), which enable behavior-coupled transcriptomics by identifying, isolating, and sequencing migratory cancer subpopulations. These approaches have led to the discovery of a novel predictive gene signature for metastasis in head and neck squamous cell carcinoma (HNSCC), as well as novel metastasis-associated mechanisms and druggable targets.
To assess TME contributions, we developed a spatially resolved single-cell sequencing method to analyze immune cell composition and pathway modulation within immune niches of metastatic vs. non-metastatic HNSCC tumors. To further enable spatial profiling of cells based on live-cell dynamics or multiple phenotypes, we are developing in situ FUNseq and multi-FUNseq platforms.
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Related references:
1. You L.*, Su P.R.*, Betjes M*. Ghadiri Rad, R., Chou, T.C., Beerens, C., van Oosten, E., Leufkens, F., Gasecka, P., Muraro, M., van Tol, R., van Steenderen, D., Farooq, S., Hardillo, J., Baatenburg de Jong, R., Brinks, D., Chien, M.P. “Linking the genotypes and phenotypes of cancer cells in heterogenous populations via real-time optical tagging and image analysis”, Nature Biomedical Engineering, 2022 (https://doi.org/10.1038/s41551-022-00853-x)
2. Brinks, D., Chien, M.P. "Functional single-cell sequencing links dynamic phenotypes to their genotypes". Nature Biomedical Engineering, 2022. (https://rdcu.be/cJr8o) (https://doi.org/10.1038/s41551-022-00877-3)
3. Chou, T.C.*, You, L.*, Beerens, C., Feller, K.F., Storteboom, J., Chien, M.P. "Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from big image data". Cell Reports Methods, 2023, DOI:https://doi.org/10.1016/j.crmeth.2023.100636.
4. Cheng, K.W., Su, P.R., Feller, K.J.A., Chien, M.P.*, Hsu, C.C.* “Investigating the Metabolic Heterogeneity of Cancer Cells Using Functional Single-Cell Selection and nLC Combined with Multinozzle Emitter Mass Spectrometry”. Analytical Chemistry. 2024, 96, 2, 624–629. DOI:https://doi.org/10.1021/acs.analchem.3c03688.
5. Ghadiri Rad, R., Lopez-Cascales, M. T., Huang, J., You, L., Traets, J.J.H., Bellomo, D., Chou, T.-C., Bennik, W., Singh J., Begce Y., Hardillo, J. A. U., Vodgama, D., van den Bosch T., van Dis, V., Chien, M.P. “Behavior-Coupled Single-Cell Transcriptomics Reveals Mechanistic Pathways Underlying Metastasis in Oral Squamous Cell Carcinoma”. Under Submission.
6. Smit, M., Feller, K.J., You, L., Chien, M.P. "Protocol for profiling intratumor heterogeneity using spatially annotated single cell sequencing." STAR Protocols. 2023, July. (DOI: 10.1016/j.xpro.2023.102447)
7. Smit M., Feller K., You L., Storeteboom J., Begce Y., Beerens C. Chien M.P. "Spatially annotated single cell sequencing for unraveling intratumor heterogeneity”, Frontiers in Bioengineering and Biotechnology, 2022 (https://doi.org/10.3389/fbioe.2022.829509).
8. Chen, T.Y., You, L. Hardillo, J.A.U., Chien, M.P. "Spatial Transcriptomic Technologies." Cells. 2023, 12(16), 2042, DOI: https://doi.org/10.3390/cells12162042.
1. You L.*, Su P.R.*, Betjes M*. Ghadiri Rad, R., Chou, T.C., Beerens, C., van Oosten, E., Leufkens, F., Gasecka, P., Muraro, M., van Tol, R., van Steenderen, D., Farooq, S., Hardillo, J., Baatenburg de Jong, R., Brinks, D., Chien, M.P. “Linking the genotypes and phenotypes of cancer cells in heterogenous populations via real-time optical tagging and image analysis”, Nature Biomedical Engineering, 2022 (https://doi.org/10.1038/s41551-022-00853-x)
2. Brinks, D., Chien, M.P. "Functional single-cell sequencing links dynamic phenotypes to their genotypes". Nature Biomedical Engineering, 2022. (https://rdcu.be/cJr8o) (https://doi.org/10.1038/s41551-022-00877-3)
3. Chou, T.C.*, You, L.*, Beerens, C., Feller, K.F., Storteboom, J., Chien, M.P. "Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from big image data". Cell Reports Methods, 2023, DOI:https://doi.org/10.1016/j.crmeth.2023.100636.
4. Cheng, K.W., Su, P.R., Feller, K.J.A., Chien, M.P.*, Hsu, C.C.* “Investigating the Metabolic Heterogeneity of Cancer Cells Using Functional Single-Cell Selection and nLC Combined with Multinozzle Emitter Mass Spectrometry”. Analytical Chemistry. 2024, 96, 2, 624–629. DOI:https://doi.org/10.1021/acs.analchem.3c03688.
5. Ghadiri Rad, R., Lopez-Cascales, M. T., Huang, J., You, L., Traets, J.J.H., Bellomo, D., Chou, T.-C., Bennik, W., Singh J., Begce Y., Hardillo, J. A. U., Vodgama, D., van den Bosch T., van Dis, V., Chien, M.P. “Behavior-Coupled Single-Cell Transcriptomics Reveals Mechanistic Pathways Underlying Metastasis in Oral Squamous Cell Carcinoma”. Under Submission.
6. Smit, M., Feller, K.J., You, L., Chien, M.P. "Protocol for profiling intratumor heterogeneity using spatially annotated single cell sequencing." STAR Protocols. 2023, July. (DOI: 10.1016/j.xpro.2023.102447)
7. Smit M., Feller K., You L., Storeteboom J., Begce Y., Beerens C. Chien M.P. "Spatially annotated single cell sequencing for unraveling intratumor heterogeneity”, Frontiers in Bioengineering and Biotechnology, 2022 (https://doi.org/10.3389/fbioe.2022.829509).
8. Chen, T.Y., You, L. Hardillo, J.A.U., Chien, M.P. "Spatial Transcriptomic Technologies." Cells. 2023, 12(16), 2042, DOI: https://doi.org/10.3390/cells12162042.
Tumor Progression
We focus on how chromosomal instability (CIN) and immune evasion contribute to tumor progression. We developed CIN-seq to selectively profile cells with CIN phenotypes and identify their associated molecular mechanisms. To distinguish causal factors from consequences of CIN, we introduced a deep learning-based model, CINet, which prospectively predicts CIN fate from early features. When combined with CIN-seq, this approach can isolate causal drivers of CIN without confounding CIN-induced resulting responses.
In parallel, we are developing computational tools to model tumor-immune cell interactions and identify how tumor cells evade immune surveillance in both 2D and 3D settings. To support this, we are extending FUNseq into 3D FUNseq, enabling selective profiling of immune-evasive cells within co-cultured organoids and tumoroids.
We focus on how chromosomal instability (CIN) and immune evasion contribute to tumor progression. We developed CIN-seq to selectively profile cells with CIN phenotypes and identify their associated molecular mechanisms. To distinguish causal factors from consequences of CIN, we introduced a deep learning-based model, CINet, which prospectively predicts CIN fate from early features. When combined with CIN-seq, this approach can isolate causal drivers of CIN without confounding CIN-induced resulting responses.
In parallel, we are developing computational tools to model tumor-immune cell interactions and identify how tumor cells evade immune surveillance in both 2D and 3D settings. To support this, we are extending FUNseq into 3D FUNseq, enabling selective profiling of immune-evasive cells within co-cultured organoids and tumoroids.
Related references:
1. Su, P.R.*, Chou, C.T.*, López-Cascales, M.T.**, Chen, T.-Y.**, You, L., Li, C., van Vliet, J., Akbarzadeh, M., van Wieren, S., Beerens, C., Chiafele, P., Storteboom, J., Derks, S., Chien, M.P. “Unraveling the molecular mechanisms underlying spontaneous chromosomal instability through CIN-seq”. Under Revision.
1. Su, P.R.*, Chou, C.T.*, López-Cascales, M.T.**, Chen, T.-Y.**, You, L., Li, C., van Vliet, J., Akbarzadeh, M., van Wieren, S., Beerens, C., Chiafele, P., Storteboom, J., Derks, S., Chien, M.P. “Unraveling the molecular mechanisms underlying spontaneous chromosomal instability through CIN-seq”. Under Revision.
Therapy Resistance
Our work on therapy resistance centers on DNA damage response (DDR)-mediated fate decision and cancer cell plasticity. Using our functional single-cell profiling platform, we have discovered two distinct irradiation-induced DDR dynamics. We are now developing and applying FUN-TRACE (FUNseq-based Transcriptional and Chromatin Exploration) to explore epigenetic and transcriptomic mechanisms differentiating these DDR responses and their link to survival and resistance in HNSCC.
To study drug tolerant persister cell plasticity, we are developing PAINT-seq, an imaging-based platform for identifying morphological signatures of drug-resistant cells. This allows us to uncover resistance-driving mechanisms without interference from drug-induced stress or secondary transcriptional responses.
Our work on therapy resistance centers on DNA damage response (DDR)-mediated fate decision and cancer cell plasticity. Using our functional single-cell profiling platform, we have discovered two distinct irradiation-induced DDR dynamics. We are now developing and applying FUN-TRACE (FUNseq-based Transcriptional and Chromatin Exploration) to explore epigenetic and transcriptomic mechanisms differentiating these DDR responses and their link to survival and resistance in HNSCC.
To study drug tolerant persister cell plasticity, we are developing PAINT-seq, an imaging-based platform for identifying morphological signatures of drug-resistant cells. This allows us to uncover resistance-driving mechanisms without interference from drug-induced stress or secondary transcriptional responses.
Related references:
1. Su, P.R., You, L., Beerens, C., Bezstarosti, K., Demmers, J., Pabst, M., Kanaar, R., Hsu, C.C., Chien, M.P. “Microscopy-based single-cell proteomic profiling reveals heterogeneity in DNA damage response dynamics”, Cell Reports Methods, 2022, https://doi.org/10.1016/j.crmeth.2022.100237.
2. Su, P.R., Chien, M.P. Functional Single-Cell Proteomics: Technology and Biological Applications. In: Callmann, C. (eds) Biomedical Nanotechnology. Methods in Molecular Biology (Springer Nature Protocols), 2025, vol 2902. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-4402-7_9.
1. Su, P.R., You, L., Beerens, C., Bezstarosti, K., Demmers, J., Pabst, M., Kanaar, R., Hsu, C.C., Chien, M.P. “Microscopy-based single-cell proteomic profiling reveals heterogeneity in DNA damage response dynamics”, Cell Reports Methods, 2022, https://doi.org/10.1016/j.crmeth.2022.100237.
2. Su, P.R., Chien, M.P. Functional Single-Cell Proteomics: Technology and Biological Applications. In: Callmann, C. (eds) Biomedical Nanotechnology. Methods in Molecular Biology (Springer Nature Protocols), 2025, vol 2902. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-4402-7_9.
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